Machine learning in finance
Machine learning promises to catapult those institutions in the financial services industry into the age of artificial intelligence. Financial institutions such as banks and insurance companies have a unique opportunity to disrupt the market and gain competitive advantage with machine learning.
Our 2021 enterprise trends in machine learning survey revealed that organizations are expanding into a wider range of AI and ML use cases; in fact, we saw a 74% year-on-year increase in the number of respondents reporting more than five applications of AI/ML at their organizations. Some of the top use cases reported are also the ones that offer the biggest opportunities for financial institutions: Customer experience improvements, process automation, and fraud detection.
Indeed, machine learning in finance presents an enormous opportunity to financial institutions to revolutionize their businesses and generate both top- and bottom-line results. Key opportunities include creating innovative and personalized customer experiences, reducing financial crimes and fraud, delivering new products, and automating manual processes.
At the same time, the financial sector is fraught with regulations and risk. It’s true that financial institutions face an imperative to invest in ML; organizations that fail to adopt this technology (or adopt it too slowly) will eventually lose all their business to competitors. However, there is also enormous risk to poorly implemented ML—and while this is the case in all industries, it is especially true in finance. Organizations that implement ML poorly, without proper operational practices such as governance, will face even greater risk than those that don’t implement it at all.
This is why financial organizations that succeed with ML are those that understand the importance of correct implementation of machine learning in finance. This is where machine learning operations (MLOps) comes into play.
Key use cases for machine learning in finance
Before we get into the specifics of MLOps, let’s take a look at the key areas where machine learning is being applied in the financial sector. Our 2021 report revealed the key categories where enterprises are implementing ML, but what does this actually look like? Here are five key ways that financial institutions are innovating with AI and ML across their businesses.
As mentioned previously, process automation is a key use case for AI and ML.
Creditworthiness is a natural and obvious use of machine learning for process automation. For decades, banks have used very rudimentary logistic regression models with inputs like income 30-60-90-day payment histories to determine likelihood of default, or the payment and interest terms of a loan.
The logistic model can be problematic as it can penalize individuals with shorter credit histories or those who work outside of traditional banking systems. Banks also miss out on additional sources of revenue from rejected borrowers who would likely be able to pay.
With the growing number of alternative data points about individuals related to their financial histories (such as rent and utility bill payments or social media actions), lenders are able to use more advanced models to make more personalized decisions about creditworthiness.
Despite the optimism around increased equitability for customers and a larger client base for banks, there is still some trepidation around using black box algorithms for making lending decisions. Regulations, including the Fair Credit Reporting Act, require creditors to give individuals specific reasons for an outcome.
However, when implemented well, algorithms for determining creditworthiness can offer enormous upside to financial institutions, while simultaneously improving customer experiences.
2. Detecting and preventing financial crimes
Machine learning in finance helps detect and reduce the incidence of financial crimes, such as online fraud and money laundering. Financial crime prevention is yet another application of machine learning for process automation, and one that’s in increasing demand. With the introduction of machine learning algorithms, organizations can now improve the detection of financial crimes with sophisticated models instead of manual processes, all while reducing processing latency to catch problems faster. Key methods include clustering and classification.
How does it work? Let’s take fraud detection as a key example.
Most fraud prevention models are based on a set of human-created rules that result in a binary classification of “fraud” or “not fraud.” The problem with these models is that they can create a high number of false positives. It’s not good for business when customers receive an abnormally high number of unnecessary fraud notifications. Trust is lost, and actual fraud may continue to go on undetected.
Machine learning clustering and classification algorithms can help reduce the problem of false positives. They continually modify the profile of a customer whenever they take a new action. With these multiple points of data, the machine can take a nuanced approach to determine what is normal and abnormal behavior.
By leveraging a modern machine learning operations (MLOps) strategy, they can move from traditional batch processing, which can take days or weeks, to real-time scoring to get results in seconds.
3. Algorithmic trading
Simply defined, algorithmic trading is automated trading using a defined set of rules. A basic example would be a trader setting up automatic buy and sell rules when a stock falls below or rises above a particular price point. More sophisticated algorithms exploit arbitrage opportunities or predict stock price fluctuations based on real-world events like mergers or regulatory approvals.
The previously mentioned models require thousands of lines of human-written code and have become increasingly unwieldy. Relying on machine learning makes trading more efficient and less prone to mistakes. It is particularly beneficial in high-frequency trading, when large volumes of orders need to be made as quickly as possible.
Automated trading has been around since the 1970s, but only recently have companies had access to the technological capabilities able to handle advanced algorithms. Many banks are investing heavily in machine learning-based trading.
4. Customer experience innovations
As revealed in our 2021 report, many enterprises are realizing the potential of machine learning to improve the customer experience, helping them attract, convert, engage, and retain more customers, more effectively.
Just a few of the many ways they’re innovating are through chatbots and robo-advisors.
Chatbots are a common use case for ML, but that doesn’t make them any less effective. Chatbots are an excellent example of natural language processing and sentiment analysis algorithms at work, and they offer an effective means to more quickly and effectively serve customers.
Another strategy to use ML to improve customer experience that can leverage similar techniques is customer service agent augmentation. By understanding the customer’s behavior and sentiment over time, live agents can better help solve their problems and offer new products and services that fit the customer profile better.
Robo-advisors have made investing and financial decision-making more accessible to the average person. Their investment strategies are derived from an algorithm based on a customer’s age, income, planned retirement date, financial goals, and risk tolerance. They typically follow traditional investment strategies and asset allocation based on that information. Because robo-advisors automate processes, they also eliminate the conflict of financial advisors not always working in a client’s best interest.
5. Cybersecurity and threat detection
Although not unique to the financial services industry, robust cybersecurity protocols are absolutely necessary to demonstrate asset safety to customers. Leveraging anomaly detection on massive datasets is a job that ML is uniquely suited for. Traditional analytic systems are just not built for the real-time demands that modern information security needs to deal with an ever-growing threat landscape. Specific examples of how machine learning is used in cybersecurity include:
- Malware detection: Algorithms can detect malicious files by flagging never-before-seen software attempting to run as unsafe.
- Insider attacks: Monitoring network traffic throughout an organization looking for anomalies like repeated attempts to access unauthorized applications or unusual keystroke behavior.
In both cases, the tedious task of constant monitoring is taken out of the hands of an employee and given to the computer. Analysts can then devote their time to conducting thorough investigations and determining the legitimacy of the threats.
Challenges of machine learning in financial services
If it seems that regulations for doing business online are constantly changing, that’s because they are. Financial services is one of the most heavily regulated industries where machine learning is being applied. Keeping up with those standards is usually on the minds of everybody on the team.
Our 2021 report revealed that most organizations face some level of regulatory burden for their machine learning, and 67% of organizations must comply with multiple regulations. This means that governing machine learning initiatives should be a top focus for most organizations—especially those in such a highly regulated industry as finance.
However, the same report showed that 56% of organizations suffer from problems surrounding governance, security, and auditability. And for some, the breadth of the issue does not truly show itself until what seems like the last minute. That’s when a jump to gain certification can oftentimes surface the volume of regulatory items that have to be addressed.
And of course, financial crimes and security present a key risk in the finance sector—that’s why they represent some of the top ML use cases in the industry. As with anything that has to do with finances, there are going to be attempts to defraud by some sort of electronic means. This is no new challenge, but how companies combat this type of activity depends on keeping up with the latest models meant to identify their signatures. Online fraud, money laundering, and general account security must all be taken into consideration.
This, again, is where machine learning operations comes in.
Benefits of MLOps in financial services
Simply put, machine learning operations—also known as MLOps—is the discipline of AI model delivery. It is a set of tools and processes that allows enterprises to industrialize, scale, and standardize their machine learning initiatives. Not only does this allow them to generate significant business value from machine learning, but it also minimizes its risk and protects the business. A critical area of focus for machine learning in finance.
Much like DevOps is typically seen as the culmination of developers and operations, MLOps is the combination of machine learning and operations. Applying the discipline of tried-and-true DevOps methodology to software delivery, MLOps enables teams to stick to their current delivery schedules and leverage existing CI/CD processes. It also enables them to implement ML policies and processes into organization-wide IT policies, designed to improve efficiency and scale while reducing risks to the business. With constantly changing regulations, MLOps ensures the latest features, fixes, and models keep up with ever-evolving regulatory, governance, and security needs. This is why all organizations that use machine learning need MLOps—but most especially those in finance.
Leading financial institutions understand that they need machine learning to succeed, and that effective ML isn’t possible without MLOps. EY and Happy Money are just a few of those organizations that are succeeding with MLOps. Within five months of implementing Algorithmia’s MLOps platform, EY developed more than 100 models using 10 different open-source frameworks and more than 75 libraries—ultimately enabling their customers to decrease the rate of financial crimes being committed. Similarly, Happy Money dramatically accelerated their model iteration time with Algorithmia, allowing them to innovate quickly to more effectively help their customers pay down personal debt.
Implement governance with MLOps
Not sure where to start when it comes to machine learning operations and governance at your financial institution? Our new whitepaper by H.P. Bunaes, founder of AI Powered Banking, presents the key considerations that risk officers need to know about governance, especially when it comes to financial services. Download it today to understand the latest governance challenges faced by the industry and how to implement a better governance strategy with Algorithmia’s MLOps platform.